{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(100)\n",
    "data = {\n",
    "    'longitude':np.random.uniform(-122.25,-122.22,size = 20640),\n",
    "    'latitude': np.random.uniform(37.85,37.88,size = 20640),\n",
    "    'housing_median_age': np.random.randint(21,52,20640),\n",
    "    'total_rooms': np.random.randint(880,7099,size = 20640),\n",
    "    'total_bedrooms': np.random.randint(129,1106,size = 20640),\n",
    "    'population': np.random.randint(322,2401,size = 20640),\n",
    "    'households': np.random.randint(126,1138,size = 20640),\n",
    "    'median_income': np.random.uniform(3.8462,8.3252,size = 20640),\n",
    "    'median_house_value': np.random.randint(341300,452600,size = 20640),\n",
    "    'ocean_proximity': ['NEAR BAY']*20640\n",
    "}\n",
    "df=pd.DataFrame(data)\n",
    "df\n",
    "df.to_csv('./housing1.csv',index = False ,sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_housing_data(housing_path = './'):\n",
    "    csv_path=os.path.join(housing_path,'housing.csv')\n",
    "    return pd.read_csv(csv_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41</td>\n",
       "      <td>880</td>\n",
       "      <td>129</td>\n",
       "      <td>322</td>\n",
       "      <td>126</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21</td>\n",
       "      <td>7099</td>\n",
       "      <td>1106</td>\n",
       "      <td>2401</td>\n",
       "      <td>1138</td>\n",
       "      <td>8.3041</td>\n",
       "      <td>358500</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1467</td>\n",
       "      <td>190</td>\n",
       "      <td>496</td>\n",
       "      <td>177</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235</td>\n",
       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1627</td>\n",
       "      <td>280</td>\n",
       "      <td>565</td>\n",
       "      <td>259</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                  41          880             129   \n",
       "1    -122.22     37.86                  21         7099            1106   \n",
       "2    -122.24     37.85                  52         1467             190   \n",
       "3    -122.25     37.85                  52         1274             235   \n",
       "4    -122.25     37.85                  52         1627             280   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0         322         126         8.3252              452600        NEAR BAY  \n",
       "1        2401        1138         8.3041              358500        NEAR BAY  \n",
       "2         496         177         7.2574              352100        NEAR BAY  \n",
       "3         558         219         5.6431              341300        NEAR BAY  \n",
       "4         565         259         3.8462              342200        NEAR BAY  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing = load_housing_data()\n",
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  int64  \n",
      " 3   total_rooms         20640 non-null  int64  \n",
      " 4   total_bedrooms      20640 non-null  int64  \n",
      " 5   population          20640 non-null  int64  \n",
      " 6   households          20640 non-null  int64  \n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   median_house_value  20640 non-null  int64  \n",
      " 9   ocean_proximity     20640 non-null  object \n",
      "dtypes: float64(3), int64(6), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info() #查看数据集的简单描述，得到每个属性类型和非空值数据量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEAR OCEAN    10668\n",
       "INLAND         5277\n",
       "NEAR BAY       3822\n",
       "<1H OCEAN       868\n",
       "ISLAND            5\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()  #查看多少种分类，5种分类，获得每种分类下有多少区域"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-122.235023</td>\n",
       "      <td>37.864953</td>\n",
       "      <td>36.005136</td>\n",
       "      <td>3977.160417</td>\n",
       "      <td>615.935320</td>\n",
       "      <td>1360.362694</td>\n",
       "      <td>630.382074</td>\n",
       "      <td>6.075214</td>\n",
       "      <td>397079.631008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.008658</td>\n",
       "      <td>0.008694</td>\n",
       "      <td>8.978258</td>\n",
       "      <td>1801.041634</td>\n",
       "      <td>280.563144</td>\n",
       "      <td>604.552303</td>\n",
       "      <td>292.162026</td>\n",
       "      <td>1.291149</td>\n",
       "      <td>32426.961457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-122.250000</td>\n",
       "      <td>37.850000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>880.000000</td>\n",
       "      <td>129.000000</td>\n",
       "      <td>322.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>3.846200</td>\n",
       "      <td>341300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-122.242568</td>\n",
       "      <td>37.857406</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>2407.000000</td>\n",
       "      <td>374.000000</td>\n",
       "      <td>838.000000</td>\n",
       "      <td>378.000000</td>\n",
       "      <td>4.963344</td>\n",
       "      <td>368911.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-122.235023</td>\n",
       "      <td>37.864898</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>3974.000000</td>\n",
       "      <td>617.000000</td>\n",
       "      <td>1359.000000</td>\n",
       "      <td>628.000000</td>\n",
       "      <td>6.077580</td>\n",
       "      <td>397149.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-122.227570</td>\n",
       "      <td>37.872565</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>5537.000000</td>\n",
       "      <td>858.000000</td>\n",
       "      <td>1885.000000</td>\n",
       "      <td>883.000000</td>\n",
       "      <td>7.184366</td>\n",
       "      <td>425660.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-122.220000</td>\n",
       "      <td>37.880000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>7099.000000</td>\n",
       "      <td>1106.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>1138.000000</td>\n",
       "      <td>8.325200</td>\n",
       "      <td>452600.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -122.235023     37.864953           36.005136   3977.160417   \n",
       "std        0.008658      0.008694            8.978258   1801.041634   \n",
       "min     -122.250000     37.850000           21.000000    880.000000   \n",
       "25%     -122.242568     37.857406           28.000000   2407.000000   \n",
       "50%     -122.235023     37.864898           36.000000   3974.000000   \n",
       "75%     -122.227570     37.872565           44.000000   5537.000000   \n",
       "max     -122.220000     37.880000           52.000000   7099.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20640.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       615.935320   1360.362694    630.382074       6.075214   \n",
       "std        280.563144    604.552303    292.162026       1.291149   \n",
       "min        129.000000    322.000000    126.000000       3.846200   \n",
       "25%        374.000000    838.000000    378.000000       4.963344   \n",
       "50%        617.000000   1359.000000    628.000000       6.077580   \n",
       "75%        858.000000   1885.000000    883.000000       7.184366   \n",
       "max       1106.000000   2401.000000   1138.000000       8.325200   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        397079.631008  \n",
       "std          32426.961457  \n",
       "min         341300.000000  \n",
       "25%         368911.500000  \n",
       "50%         397149.000000  \n",
       "75%         425660.500000  \n",
       "max         452600.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.describe() #显示数值属性摘要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#魔法命令，底层调用matplotlib，在jupyter格子中绘制图形，将jupyter作为matplotalib后段显示\n",
    "%matplotlib inline  \n",
    "import matplotlib.pyplot as plt \n",
    "housing.hist(bins = 50,figsize = (20,15))\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 按照教程中数据，房龄和房价被设定上线\n",
    "   1. 设置上线区域进行重新收集标签纸\n",
    "   2. 把设置了上线（50万美元）的区域数据移除\n",
    "   \n",
    "### 重尾 头高尾长\n",
    "   * 转换数据，把数据形状变成偏钟形分布（正态分布，平均值为0，标准差为1）\n",
    "### 收入特征\n",
    "   * 数据提供的上游证实是年薪单位万美元，提前对待特征进行缩放是正确的，需要得知数据是如何缩放的\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建训练集和测试集\n",
    "   * 训练集用于模型训练 占整个数据集的80%，测试占20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data,test_radio):\n",
    "    np.random.seed(2)\n",
    "    #给一串整数，重新洗牌，随机打乱原来元素顺序，生成随机序列\n",
    "    indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_radio)\n",
    "    test_indices = indices[:test_set_size]\n",
    "    train_indices = indices[test_set_size:]\n",
    "    #通过物理索引来取值\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set , test_set = split_train_test(housing,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16512"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4128"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 如何对数据2、8分：对样本设置唯一的标识符，对标识符hash值，去hash的最后一个字节，值小于等于51，256*20%，放入测试集\n",
    "# 如果hash值相等的对象，对象不一定相同，如果hash值不同，一定不是相同对象\n",
    "\n",
    "import hashlib\n",
    "\n",
    "def test_set_check(identifier,test_radio,hash = hashlib.md5):\n",
    "    return hash(np.int64(identifier)).digest()[-1] <256*test_radio  # digest()方法取得hash值二进制的对象；整句话返回摘要，作为二进制数据字符串"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_train_test_by_id(data,test_ratio,id_column):\n",
    "    ids = data[id_column]\n",
    "    in_test_set = ids.apply(lambda id_:test_set_check(id_,test_ratio))\n",
    "    return data.loc[~in_test_set],data.loc[in_test_set]  #~表示取反"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41</td>\n",
       "      <td>880</td>\n",
       "      <td>129</td>\n",
       "      <td>322</td>\n",
       "      <td>126</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21</td>\n",
       "      <td>7099</td>\n",
       "      <td>1106</td>\n",
       "      <td>2401</td>\n",
       "      <td>1138</td>\n",
       "      <td>8.3041</td>\n",
       "      <td>358500</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1467</td>\n",
       "      <td>190</td>\n",
       "      <td>496</td>\n",
       "      <td>177</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235</td>\n",
       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1627</td>\n",
       "      <td>280</td>\n",
       "      <td>565</td>\n",
       "      <td>259</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                  41          880             129   \n",
       "1    -122.22     37.86                  21         7099            1106   \n",
       "2    -122.24     37.85                  52         1467             190   \n",
       "3    -122.25     37.85                  52         1274             235   \n",
       "4    -122.25     37.85                  52         1627             280   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0         322         126         8.3252              452600        NEAR BAY  \n",
       "1        2401        1138         8.3041              358500        NEAR BAY  \n",
       "2         496         177         7.2574              352100        NEAR BAY  \n",
       "3         558         219         5.6431              341300        NEAR BAY  \n",
       "4         565         259         3.8462              342200        NEAR BAY  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id = housing.reset_index()  #使用行索引作为标识符ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "      <td>7.257400</td>\n",
       "      <td>352100</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.250000</td>\n",
       "      <td>37.850000</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
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       "      <td>219</td>\n",
       "      <td>5.643100</td>\n",
       "      <td>341300</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
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       "      <td>565</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>20635</td>\n",
       "      <td>-122.238929</td>\n",
       "      <td>37.872450</td>\n",
       "      <td>31</td>\n",
       "      <td>5719</td>\n",
       "      <td>471</td>\n",
       "      <td>1349</td>\n",
       "      <td>936</td>\n",
       "      <td>6.109815</td>\n",
       "      <td>449158</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>20636</td>\n",
       "      <td>-122.246666</td>\n",
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       "      <td>22</td>\n",
       "      <td>1518</td>\n",
       "      <td>226</td>\n",
       "      <td>947</td>\n",
       "      <td>1020</td>\n",
       "      <td>6.684759</td>\n",
       "      <td>448958</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>20637</td>\n",
       "      <td>-122.233162</td>\n",
       "      <td>37.866991</td>\n",
       "      <td>51</td>\n",
       "      <td>2245</td>\n",
       "      <td>666</td>\n",
       "      <td>2327</td>\n",
       "      <td>138</td>\n",
       "      <td>8.012214</td>\n",
       "      <td>392108</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>20638</td>\n",
       "      <td>-122.233140</td>\n",
       "      <td>37.855112</td>\n",
       "      <td>28</td>\n",
       "      <td>1776</td>\n",
       "      <td>356</td>\n",
       "      <td>610</td>\n",
       "      <td>449</td>\n",
       "      <td>8.081880</td>\n",
       "      <td>389614</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
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       "      <td>130</td>\n",
       "      <td>4.770615</td>\n",
       "      <td>355427</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index   longitude   latitude  housing_median_age  total_rooms  \\\n",
       "0          0 -122.230000  37.880000                  41          880   \n",
       "1          1 -122.220000  37.860000                  21         7099   \n",
       "2          2 -122.240000  37.850000                  52         1467   \n",
       "3          3 -122.250000  37.850000                  52         1274   \n",
       "4          4 -122.250000  37.850000                  52         1627   \n",
       "...      ...         ...        ...                 ...          ...   \n",
       "20635  20635 -122.238929  37.872450                  31         5719   \n",
       "20636  20636 -122.246666  37.855190                  22         1518   \n",
       "20637  20637 -122.233162  37.866991                  51         2245   \n",
       "20638  20638 -122.233140  37.855112                  28         1776   \n",
       "20639  20639 -122.245868  37.872594                  51         2200   \n",
       "\n",
       "       total_bedrooms  population  households  median_income  \\\n",
       "0                 129         322         126       8.325200   \n",
       "1                1106        2401        1138       8.304100   \n",
       "2                 190         496         177       7.257400   \n",
       "3                 235         558         219       5.643100   \n",
       "4                 280         565         259       3.846200   \n",
       "...               ...         ...         ...            ...   \n",
       "20635             471        1349         936       6.109815   \n",
       "20636             226         947        1020       6.684759   \n",
       "20637             666        2327         138       8.012214   \n",
       "20638             356         610         449       8.081880   \n",
       "20639             988         562         130       4.770615   \n",
       "\n",
       "       median_house_value ocean_proximity  \n",
       "0                  452600        NEAR BAY  \n",
       "1                  358500        NEAR BAY  \n",
       "2                  352100        NEAR BAY  \n",
       "3                  341300        NEAR BAY  \n",
       "4                  342200        NEAR BAY  \n",
       "...                   ...             ...  \n",
       "20635              449158        NEAR BAY  \n",
       "20636              448958        NEAR BAY  \n",
       "20637              392108        NEAR BAY  \n",
       "20638              389614        NEAR BAY  \n",
       "20639              355427        NEAR BAY  \n",
       "\n",
       "[20640 rows x 11 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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   ],
   "source": [
    "housing_with_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set = split_train_test_by_id(housing_with_id, 0.2,'index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>352100</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>37.850000</td>\n",
       "      <td>52</td>\n",
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       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.643100</td>\n",
       "      <td>341300</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>-122.229878</td>\n",
       "      <td>37.879925</td>\n",
       "      <td>28</td>\n",
       "      <td>5755</td>\n",
       "      <td>238</td>\n",
       "      <td>665</td>\n",
       "      <td>866</td>\n",
       "      <td>5.094433</td>\n",
       "      <td>429974</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   longitude   latitude  housing_median_age  total_rooms  \\\n",
       "0      0 -122.230000  37.880000                  41          880   \n",
       "1      1 -122.220000  37.860000                  21         7099   \n",
       "2      2 -122.240000  37.850000                  52         1467   \n",
       "3      3 -122.250000  37.850000                  52         1274   \n",
       "6      6 -122.229878  37.879925                  28         5755   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0             129         322         126       8.325200              452600   \n",
       "1            1106        2401        1138       8.304100              358500   \n",
       "2             190         496         177       7.257400              352100   \n",
       "3             235         558         219       5.643100              341300   \n",
       "6             238         665         866       5.094433              429974   \n",
       "\n",
       "  ocean_proximity  \n",
       "0        NEAR BAY  \n",
       "1        NEAR BAY  \n",
       "2        NEAR BAY  \n",
       "3        NEAR BAY  \n",
       "6        NEAR BAY  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#基于行索引，不能删除和中间插入数据，只能末尾插入，否则行索引会变\n",
    "#寻找稳定特征来创建唯一标识符，"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing_with_id['id'] = housing['longitude'] * 1000 +housing['latitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <td>322</td>\n",
       "      <td>126</td>\n",
       "      <td>8.325200</td>\n",
       "      <td>452600</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.220000</td>\n",
       "      <td>37.860000</td>\n",
       "      <td>21</td>\n",
       "      <td>7099</td>\n",
       "      <td>1106</td>\n",
       "      <td>2401</td>\n",
       "      <td>1138</td>\n",
       "      <td>8.304100</td>\n",
       "      <td>358500</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.240000</td>\n",
       "      <td>37.850000</td>\n",
       "      <td>52</td>\n",
       "      <td>1467</td>\n",
       "      <td>190</td>\n",
       "      <td>496</td>\n",
       "      <td>177</td>\n",
       "      <td>7.257400</td>\n",
       "      <td>352100</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.250000</td>\n",
       "      <td>37.850000</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235</td>\n",
       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.643100</td>\n",
       "      <td>341300</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.250000</td>\n",
       "      <td>37.850000</td>\n",
       "      <td>52</td>\n",
       "      <td>1627</td>\n",
       "      <td>280</td>\n",
       "      <td>565</td>\n",
       "      <td>259</td>\n",
       "      <td>3.846200</td>\n",
       "      <td>342200</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>20635</td>\n",
       "      <td>-122.238929</td>\n",
       "      <td>37.872450</td>\n",
       "      <td>31</td>\n",
       "      <td>5719</td>\n",
       "      <td>471</td>\n",
       "      <td>1349</td>\n",
       "      <td>936</td>\n",
       "      <td>6.109815</td>\n",
       "      <td>449158</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122201.056197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>20636</td>\n",
       "      <td>-122.246666</td>\n",
       "      <td>37.855190</td>\n",
       "      <td>22</td>\n",
       "      <td>1518</td>\n",
       "      <td>226</td>\n",
       "      <td>947</td>\n",
       "      <td>1020</td>\n",
       "      <td>6.684759</td>\n",
       "      <td>448958</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122208.810548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>20637</td>\n",
       "      <td>-122.233162</td>\n",
       "      <td>37.866991</td>\n",
       "      <td>51</td>\n",
       "      <td>2245</td>\n",
       "      <td>666</td>\n",
       "      <td>2327</td>\n",
       "      <td>138</td>\n",
       "      <td>8.012214</td>\n",
       "      <td>392108</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122195.295389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>20638</td>\n",
       "      <td>-122.233140</td>\n",
       "      <td>37.855112</td>\n",
       "      <td>28</td>\n",
       "      <td>1776</td>\n",
       "      <td>356</td>\n",
       "      <td>610</td>\n",
       "      <td>449</td>\n",
       "      <td>8.081880</td>\n",
       "      <td>389614</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122195.284830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>20639</td>\n",
       "      <td>-122.245868</td>\n",
       "      <td>37.872594</td>\n",
       "      <td>51</td>\n",
       "      <td>2200</td>\n",
       "      <td>988</td>\n",
       "      <td>562</td>\n",
       "      <td>130</td>\n",
       "      <td>4.770615</td>\n",
       "      <td>355427</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122207.995482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       index   longitude   latitude  housing_median_age  total_rooms  \\\n",
       "0          0 -122.230000  37.880000                  41          880   \n",
       "1          1 -122.220000  37.860000                  21         7099   \n",
       "2          2 -122.240000  37.850000                  52         1467   \n",
       "3          3 -122.250000  37.850000                  52         1274   \n",
       "4          4 -122.250000  37.850000                  52         1627   \n",
       "...      ...         ...        ...                 ...          ...   \n",
       "20635  20635 -122.238929  37.872450                  31         5719   \n",
       "20636  20636 -122.246666  37.855190                  22         1518   \n",
       "20637  20637 -122.233162  37.866991                  51         2245   \n",
       "20638  20638 -122.233140  37.855112                  28         1776   \n",
       "20639  20639 -122.245868  37.872594                  51         2200   \n",
       "\n",
       "       total_bedrooms  population  households  median_income  \\\n",
       "0                 129         322         126       8.325200   \n",
       "1                1106        2401        1138       8.304100   \n",
       "2                 190         496         177       7.257400   \n",
       "3                 235         558         219       5.643100   \n",
       "4                 280         565         259       3.846200   \n",
       "...               ...         ...         ...            ...   \n",
       "20635             471        1349         936       6.109815   \n",
       "20636             226         947        1020       6.684759   \n",
       "20637             666        2327         138       8.012214   \n",
       "20638             356         610         449       8.081880   \n",
       "20639             988         562         130       4.770615   \n",
       "\n",
       "       median_house_value ocean_proximity             id  \n",
       "0                  452600        NEAR BAY -122192.120000  \n",
       "1                  358500        NEAR BAY -122182.140000  \n",
       "2                  352100        NEAR BAY -122202.150000  \n",
       "3                  341300        NEAR BAY -122212.150000  \n",
       "4                  342200        NEAR BAY -122212.150000  \n",
       "...                   ...             ...            ...  \n",
       "20635              449158        NEAR BAY -122201.056197  \n",
       "20636              448958        NEAR BAY -122208.810548  \n",
       "20637              392108        NEAR BAY -122195.295389  \n",
       "20638              389614        NEAR BAY -122195.284830  \n",
       "20639              355427        NEAR BAY -122207.995482  \n",
       "\n",
       "[20640 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_with_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set,test_set = split_train_test_by_id(housing_with_id, 0.2,'id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41</td>\n",
       "      <td>880</td>\n",
       "      <td>129</td>\n",
       "      <td>322</td>\n",
       "      <td>126</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122192.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21</td>\n",
       "      <td>7099</td>\n",
       "      <td>1106</td>\n",
       "      <td>2401</td>\n",
       "      <td>1138</td>\n",
       "      <td>8.3041</td>\n",
       "      <td>358500</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122182.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1467</td>\n",
       "      <td>190</td>\n",
       "      <td>496</td>\n",
       "      <td>177</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122202.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235</td>\n",
       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1627</td>\n",
       "      <td>280</td>\n",
       "      <td>565</td>\n",
       "      <td>259</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>-122212.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  longitude  latitude  housing_median_age  total_rooms  \\\n",
       "0      0    -122.23     37.88                  41          880   \n",
       "1      1    -122.22     37.86                  21         7099   \n",
       "2      2    -122.24     37.85                  52         1467   \n",
       "3      3    -122.25     37.85                  52         1274   \n",
       "4      4    -122.25     37.85                  52         1627   \n",
       "\n",
       "   total_bedrooms  population  households  median_income  median_house_value  \\\n",
       "0             129         322         126         8.3252              452600   \n",
       "1            1106        2401        1138         8.3041              358500   \n",
       "2             190         496         177         7.2574              352100   \n",
       "3             235         558         219         5.6431              341300   \n",
       "4             280         565         259         3.8462              342200   \n",
       "\n",
       "  ocean_proximity         id  \n",
       "0        NEAR BAY -122192.12  \n",
       "1        NEAR BAY -122182.14  \n",
       "2        NEAR BAY -122202.15  \n",
       "3        NEAR BAY -122212.15  \n",
       "4        NEAR BAY -122212.15  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从数据可视化中探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分层采样"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 抽样偏差：调查公司给1000个人来调研几个问题，不会在电话簿中随机查找1000人，他们视图确保1000人代表全体人口，美国人，51.3女性，48.7男性\n",
    "#你的调查应该维持这一比例，513名女性，487男性，这就是分层采样，人口划分为均匀的子集，每个子集称为一层，然后从每层中抽取正确的实际数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing['median_income'].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#希望测试集能够代表整个数据集中各种不同类型的收入"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   1. 创建一个收入类别属性\n",
    "   2. 不应该数据分层太多，但每一层应该有足够的数据量\n",
    "   3. 将收入中位数处以1.5，限制收入类别数量，使用ceil取整，得到离散类别，将大于5的列别合并为类别5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['income_cat'] = np.ceil(housing['median_income']/1.5)\n",
    "housing['income_cat'].head(20)\n",
    "\n",
    "housing['income_cat'].where(housing['income_cat']<5,5.0,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5.0\n",
       "1     5.0\n",
       "2     5.0\n",
       "3     4.0\n",
       "4     3.0\n",
       "5     4.0\n",
       "6     4.0\n",
       "7     5.0\n",
       "8     4.0\n",
       "9     5.0\n",
       "10    5.0\n",
       "11    3.0\n",
       "12    5.0\n",
       "13    4.0\n",
       "14    3.0\n",
       "15    4.0\n",
       "16    4.0\n",
       "17    5.0\n",
       "18    4.0\n",
       "19    4.0\n",
       "Name: income_cat, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把固定的连续值的那一列转化为类别标签\n",
    "housing['median_income_cat'] = pd.cut(housing['median_income'],bins= [0.,1.5,3.0,4.5,6.,np.inf],labels = [1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     5\n",
       "2     5\n",
       "3     4\n",
       "4     3\n",
       "5     4\n",
       "6     4\n",
       "7     5\n",
       "8     4\n",
       "9     5\n",
       "10    5\n",
       "11    3\n",
       "12    5\n",
       "13    4\n",
       "14    3\n",
       "15    4\n",
       "16    4\n",
       "17    5\n",
       "18    4\n",
       "19    4\n",
       "Name: median_income_cat, dtype: category\n",
       "Categories (5, int64): [1 < 2 < 3 < 4 < 5]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['median_income_cat'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    10664\n",
       "4     6958\n",
       "3     3018\n",
       "2        0\n",
       "1        0\n",
       "Name: median_income_cat, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['median_income_cat'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114647510>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#收入类别直方图\n",
    "housing['median_income_cat'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#根据收入类别进行分层采样\n",
    "from sklearn.model_selection import StratifiedShuffleSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = StratifiedShuffleSplit(n_splits=1,test_size = 0.2,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index,test_index in split.split(housing,housing['income_cat']):\n",
    "    strat_train_set = housing.loc[train_index]\n",
    "    strat_test_set = housing.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16512, 12)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>median_income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12381</th>\n",
       "      <td>-122.245973</td>\n",
       "      <td>37.855985</td>\n",
       "      <td>27</td>\n",
       "      <td>2582</td>\n",
       "      <td>512</td>\n",
       "      <td>409</td>\n",
       "      <td>146</td>\n",
       "      <td>8.075371</td>\n",
       "      <td>443344</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15153</th>\n",
       "      <td>-122.221184</td>\n",
       "      <td>37.873214</td>\n",
       "      <td>47</td>\n",
       "      <td>2407</td>\n",
       "      <td>904</td>\n",
       "      <td>345</td>\n",
       "      <td>776</td>\n",
       "      <td>8.219443</td>\n",
       "      <td>387409</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8891</th>\n",
       "      <td>-122.238933</td>\n",
       "      <td>37.877959</td>\n",
       "      <td>43</td>\n",
       "      <td>3436</td>\n",
       "      <td>314</td>\n",
       "      <td>850</td>\n",
       "      <td>141</td>\n",
       "      <td>7.498699</td>\n",
       "      <td>392157</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7020</th>\n",
       "      <td>-122.224909</td>\n",
       "      <td>37.862056</td>\n",
       "      <td>44</td>\n",
       "      <td>1567</td>\n",
       "      <td>296</td>\n",
       "      <td>2335</td>\n",
       "      <td>498</td>\n",
       "      <td>6.024719</td>\n",
       "      <td>387497</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>-122.247946</td>\n",
       "      <td>37.856229</td>\n",
       "      <td>29</td>\n",
       "      <td>5326</td>\n",
       "      <td>340</td>\n",
       "      <td>363</td>\n",
       "      <td>477</td>\n",
       "      <td>7.758910</td>\n",
       "      <td>369946</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5632</th>\n",
       "      <td>-122.221641</td>\n",
       "      <td>37.863319</td>\n",
       "      <td>44</td>\n",
       "      <td>5116</td>\n",
       "      <td>140</td>\n",
       "      <td>623</td>\n",
       "      <td>785</td>\n",
       "      <td>4.621779</td>\n",
       "      <td>408976</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12574</th>\n",
       "      <td>-122.249137</td>\n",
       "      <td>37.866069</td>\n",
       "      <td>21</td>\n",
       "      <td>2292</td>\n",
       "      <td>1038</td>\n",
       "      <td>823</td>\n",
       "      <td>1082</td>\n",
       "      <td>6.249910</td>\n",
       "      <td>389816</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>-122.239105</td>\n",
       "      <td>37.855522</td>\n",
       "      <td>27</td>\n",
       "      <td>3283</td>\n",
       "      <td>603</td>\n",
       "      <td>734</td>\n",
       "      <td>1123</td>\n",
       "      <td>4.141321</td>\n",
       "      <td>435425</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11532</th>\n",
       "      <td>-122.221600</td>\n",
       "      <td>37.861837</td>\n",
       "      <td>41</td>\n",
       "      <td>6925</td>\n",
       "      <td>603</td>\n",
       "      <td>1089</td>\n",
       "      <td>920</td>\n",
       "      <td>7.539890</td>\n",
       "      <td>442615</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14274</th>\n",
       "      <td>-122.237797</td>\n",
       "      <td>37.851516</td>\n",
       "      <td>43</td>\n",
       "      <td>6422</td>\n",
       "      <td>439</td>\n",
       "      <td>1472</td>\n",
       "      <td>801</td>\n",
       "      <td>3.886333</td>\n",
       "      <td>422207</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19943</th>\n",
       "      <td>-122.238832</td>\n",
       "      <td>37.865431</td>\n",
       "      <td>37</td>\n",
       "      <td>2228</td>\n",
       "      <td>412</td>\n",
       "      <td>2322</td>\n",
       "      <td>704</td>\n",
       "      <td>5.958382</td>\n",
       "      <td>446813</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13490</th>\n",
       "      <td>-122.225008</td>\n",
       "      <td>37.862479</td>\n",
       "      <td>35</td>\n",
       "      <td>2554</td>\n",
       "      <td>390</td>\n",
       "      <td>982</td>\n",
       "      <td>604</td>\n",
       "      <td>5.753763</td>\n",
       "      <td>399345</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15239</th>\n",
       "      <td>-122.230767</td>\n",
       "      <td>37.873777</td>\n",
       "      <td>26</td>\n",
       "      <td>3871</td>\n",
       "      <td>698</td>\n",
       "      <td>2271</td>\n",
       "      <td>1065</td>\n",
       "      <td>5.374034</td>\n",
       "      <td>442171</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>-122.239341</td>\n",
       "      <td>37.869285</td>\n",
       "      <td>21</td>\n",
       "      <td>6803</td>\n",
       "      <td>716</td>\n",
       "      <td>1055</td>\n",
       "      <td>759</td>\n",
       "      <td>7.609129</td>\n",
       "      <td>381169</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17906</th>\n",
       "      <td>-122.240759</td>\n",
       "      <td>37.872274</td>\n",
       "      <td>41</td>\n",
       "      <td>5427</td>\n",
       "      <td>662</td>\n",
       "      <td>1491</td>\n",
       "      <td>923</td>\n",
       "      <td>5.517844</td>\n",
       "      <td>446522</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15987</th>\n",
       "      <td>-122.247955</td>\n",
       "      <td>37.851299</td>\n",
       "      <td>21</td>\n",
       "      <td>3380</td>\n",
       "      <td>223</td>\n",
       "      <td>780</td>\n",
       "      <td>300</td>\n",
       "      <td>7.520979</td>\n",
       "      <td>414215</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10703</th>\n",
       "      <td>-122.244306</td>\n",
       "      <td>37.879474</td>\n",
       "      <td>27</td>\n",
       "      <td>4785</td>\n",
       "      <td>733</td>\n",
       "      <td>2339</td>\n",
       "      <td>458</td>\n",
       "      <td>5.320697</td>\n",
       "      <td>447667</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17289</th>\n",
       "      <td>-122.224903</td>\n",
       "      <td>37.859061</td>\n",
       "      <td>25</td>\n",
       "      <td>3065</td>\n",
       "      <td>1089</td>\n",
       "      <td>1196</td>\n",
       "      <td>576</td>\n",
       "      <td>4.223127</td>\n",
       "      <td>361736</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18253</th>\n",
       "      <td>-122.236401</td>\n",
       "      <td>37.859405</td>\n",
       "      <td>23</td>\n",
       "      <td>6416</td>\n",
       "      <td>269</td>\n",
       "      <td>985</td>\n",
       "      <td>535</td>\n",
       "      <td>7.882356</td>\n",
       "      <td>424459</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14018</th>\n",
       "      <td>-122.238716</td>\n",
       "      <td>37.855511</td>\n",
       "      <td>48</td>\n",
       "      <td>991</td>\n",
       "      <td>437</td>\n",
       "      <td>2317</td>\n",
       "      <td>637</td>\n",
       "      <td>4.310985</td>\n",
       "      <td>350675</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        longitude   latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "12381 -122.245973  37.855985                  27         2582             512   \n",
       "15153 -122.221184  37.873214                  47         2407             904   \n",
       "8891  -122.238933  37.877959                  43         3436             314   \n",
       "7020  -122.224909  37.862056                  44         1567             296   \n",
       "2832  -122.247946  37.856229                  29         5326             340   \n",
       "5632  -122.221641  37.863319                  44         5116             140   \n",
       "12574 -122.249137  37.866069                  21         2292            1038   \n",
       "14976 -122.239105  37.855522                  27         3283             603   \n",
       "11532 -122.221600  37.861837                  41         6925             603   \n",
       "14274 -122.237797  37.851516                  43         6422             439   \n",
       "19943 -122.238832  37.865431                  37         2228             412   \n",
       "13490 -122.225008  37.862479                  35         2554             390   \n",
       "15239 -122.230767  37.873777                  26         3871             698   \n",
       "1353  -122.239341  37.869285                  21         6803             716   \n",
       "17906 -122.240759  37.872274                  41         5427             662   \n",
       "15987 -122.247955  37.851299                  21         3380             223   \n",
       "10703 -122.244306  37.879474                  27         4785             733   \n",
       "17289 -122.224903  37.859061                  25         3065            1089   \n",
       "18253 -122.236401  37.859405                  23         6416             269   \n",
       "14018 -122.238716  37.855511                  48          991             437   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "12381         409         146       8.075371              443344   \n",
       "15153         345         776       8.219443              387409   \n",
       "8891          850         141       7.498699              392157   \n",
       "7020         2335         498       6.024719              387497   \n",
       "2832          363         477       7.758910              369946   \n",
       "5632          623         785       4.621779              408976   \n",
       "12574         823        1082       6.249910              389816   \n",
       "14976         734        1123       4.141321              435425   \n",
       "11532        1089         920       7.539890              442615   \n",
       "14274        1472         801       3.886333              422207   \n",
       "19943        2322         704       5.958382              446813   \n",
       "13490         982         604       5.753763              399345   \n",
       "15239        2271        1065       5.374034              442171   \n",
       "1353         1055         759       7.609129              381169   \n",
       "17906        1491         923       5.517844              446522   \n",
       "15987         780         300       7.520979              414215   \n",
       "10703        2339         458       5.320697              447667   \n",
       "17289        1196         576       4.223127              361736   \n",
       "18253         985         535       7.882356              424459   \n",
       "14018        2317         637       4.310985              350675   \n",
       "\n",
       "      ocean_proximity  income_cat median_income_cat  \n",
       "12381        NEAR BAY         5.0                 5  \n",
       "15153      NEAR OCEAN         5.0                 5  \n",
       "8891       NEAR OCEAN         5.0                 5  \n",
       "7020       NEAR OCEAN         5.0                 5  \n",
       "2832           INLAND         5.0                 5  \n",
       "5632       NEAR OCEAN         4.0                 4  \n",
       "12574        NEAR BAY         5.0                 5  \n",
       "14976      NEAR OCEAN         3.0                 3  \n",
       "11532      NEAR OCEAN         5.0                 5  \n",
       "14274      NEAR OCEAN         3.0                 3  \n",
       "19943        NEAR BAY         4.0                 4  \n",
       "13490        NEAR BAY         4.0                 4  \n",
       "15239      NEAR OCEAN         4.0                 4  \n",
       "1353           INLAND         5.0                 5  \n",
       "17906        NEAR BAY         4.0                 4  \n",
       "15987      NEAR OCEAN         5.0                 5  \n",
       "10703      NEAR OCEAN         4.0                 4  \n",
       "17289      NEAR OCEAN         3.0                 3  \n",
       "18253       <1H OCEAN         5.0                 5  \n",
       "14018      NEAR OCEAN         3.0                 3  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "strat_train_set.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    0.516715\n",
       "4    0.337209\n",
       "3    0.146076\n",
       "2    0.000000\n",
       "1    0.000000\n",
       "Name: median_income_cat, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看所有住房数据根据收入类别比例分布，收入占类别百分比\n",
    "strat_test_set['median_income_cat'].value_counts()/len(strat_test_set) #分层抽样测试集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    0.516667\n",
       "4    0.337112\n",
       "3    0.146221\n",
       "2    0.000000\n",
       "1    0.000000\n",
       "Name: median_income_cat, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['median_income_cat'].value_counts()/len(housing) #完整数据集合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_test_split' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-435eabfefc3b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"median_income_cat\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtrain_set\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_set\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m compart_props = pd.DataFrame({\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_test_split' is not defined"
     ]
    }
   ],
   "source": [
    "def income_cat_proportions(data):\n",
    "    return data[\"median_income_cat\"].value_counts()/len(data)\n",
    "\n",
    "train_set, test_set = train_test_split(housing,test_size=0.2 , random_state=42)\n",
    "\n",
    "compart_props = pd.DataFrame({\n",
    "    \"全部数据\":income_cat_proportions(housing),\n",
    "    \"分层数据\":income_cat_proportions(strat_test_set),\n",
    "    \"随机抽样\":income_cat_proportions(test_set),\n",
    "}).sort_index()\n",
    "compare_props[\"随机. %error\"] = 100*compare_props[\"随机抽样\"]/compare_props[\"全部数据\"]-100\n",
    "compare_props[\"分层. %error\"] = 100*compare_props[\"分层抽样\"]/compare_props[\"全部数据\"]-100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'compare_prope' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-98e5b358d51b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcompare_prope\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'compare_prope' is not defined"
     ]
    }
   ],
   "source": [
    "compare_prope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把测试集已经处理完成，用分层抽样的训练集数据来进行分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将地理数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#建立各个区域的分布图，以便于数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11349fa50>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind='scatter',x='longitude',y = 'latitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#将alpha设置为0.1，可以看出高密度数据点的位置\n",
    "housing.plot(kind = 'scatter',x = 'longitude',y = 'latitude',alpha=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11458e910>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#我们的大脑非常善于从图片中发展模式，但是需要玩转可视化参数才能让这些模式凸显出来\n",
    "housing.plot(kind = 'scatter',x = 'longitude',y = 'latitude',alpha=0.4,\n",
    "    s=housing[\"population\"]/100,label=\"population\",figsize=(10,7),\n",
    "    c=\"median_house_value\",cmap=plt.get_cmap(\"jet\"),colorbar=True,  #plt.get_cmap(\"jet\")画颜色，colorbar最右边色条\n",
    "    sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid PNG header",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-d06e4bbf3ed3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimage\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmpimg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcalifornia_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./california.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m housing.plot(kind = 'scatter',x = 'longitude',y = 'latitude',alpha=0.4,\n\u001b[1;32m      5\u001b[0m     \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhousing\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"population\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"population\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Downloads/Python/Practice/jupyter notebook practice/venv/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mimread\u001b[0;34m(fname, format)\u001b[0m\n\u001b[1;32m   1432\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1433\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1434\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mhandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1435\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1436\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Downloads/Python/Practice/jupyter notebook practice/venv/lib/python3.7/site-packages/matplotlib/image.py\u001b[0m in \u001b[0;36mread_png\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1388\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1389\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_png\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1390\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_png\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_png\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1392\u001b[0m     \u001b[0mhandlers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'png'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mread_png\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid PNG header"
     ]
    }
   ],
   "source": [
    "import matplotlib.image as mpimg\n",
    "california_img = mpimg.imread('./california.png')\n",
    "\n",
    "housing.plot(kind = 'scatter',x = 'longitude',y = 'latitude',alpha=0.4,\n",
    "    s=housing[\"population\"]/100,label=\"population\",figsize=(10,7),\n",
    "    c=\"median_house_value\",cmap=plt.get_cmap(\"jet\"),colorbar=False,  \n",
    "    sharex=False)\n",
    "\n",
    "plt.imshow(california_img,extent = [-0.055,-0.015,38.85,37.88],alpha = 0.5,\n",
    "          cmap=plt.get_cmap(\"jet\"))\n",
    "plt.ylabel(\"Latitude\",fontsize=14)\n",
    "plt.xlabel(\"Longitude\",fontsize=14)\n",
    "\n",
    "prices = housing[\"median_house_value\"]\n",
    "tick_values = np.linspace(prices.min(),prices.max(),11)  #最大最小值之间11等分，赋值给tick_values\n",
    "cbar = plt.colorbar()\n",
    "cbar.ax.set_yticklabels([\"$%dk\"%(round(v/1000)) for v in tick_values],fontsize = 14)\n",
    "cbar.set_label('Median House Value',fontsize = 16)\n",
    "\n",
    "plt.legend(fontsize = 16)\n",
    "#save_fig(\"california_housing_prices_plot\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#从图片中告诉我们，房屋价格与地理位置靠海近，和人口密度息息相关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 寻找特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000891</td>\n",
       "      <td>-0.004574</td>\n",
       "      <td>0.003629</td>\n",
       "      <td>-0.001897</td>\n",
       "      <td>-0.006753</td>\n",
       "      <td>0.004633</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.009117</td>\n",
       "      <td>0.002276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>0.000891</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009445</td>\n",
       "      <td>0.012716</td>\n",
       "      <td>0.000998</td>\n",
       "      <td>-0.012070</td>\n",
       "      <td>0.010274</td>\n",
       "      <td>0.001776</td>\n",
       "      <td>-0.005176</td>\n",
       "      <td>0.004711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>-0.004574</td>\n",
       "      <td>0.009445</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005760</td>\n",
       "      <td>-0.002436</td>\n",
       "      <td>-0.003759</td>\n",
       "      <td>-0.007078</td>\n",
       "      <td>0.002142</td>\n",
       "      <td>-0.010343</td>\n",
       "      <td>0.000690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>0.003629</td>\n",
       "      <td>0.012716</td>\n",
       "      <td>-0.005760</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005722</td>\n",
       "      <td>0.014791</td>\n",
       "      <td>-0.003304</td>\n",
       "      <td>-0.000532</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>-0.000997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>-0.001897</td>\n",
       "      <td>0.000998</td>\n",
       "      <td>-0.002436</td>\n",
       "      <td>-0.005722</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.002994</td>\n",
       "      <td>0.000655</td>\n",
       "      <td>-0.006225</td>\n",
       "      <td>-0.001047</td>\n",
       "      <td>-0.005692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>-0.006753</td>\n",
       "      <td>-0.012070</td>\n",
       "      <td>-0.003759</td>\n",
       "      <td>0.014791</td>\n",
       "      <td>-0.002994</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.010723</td>\n",
       "      <td>-0.001956</td>\n",
       "      <td>0.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>0.004633</td>\n",
       "      <td>0.010274</td>\n",
       "      <td>-0.007078</td>\n",
       "      <td>-0.003304</td>\n",
       "      <td>0.000655</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.007847</td>\n",
       "      <td>0.010198</td>\n",
       "      <td>-0.006634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.001776</td>\n",
       "      <td>0.002142</td>\n",
       "      <td>-0.000532</td>\n",
       "      <td>-0.006225</td>\n",
       "      <td>0.010723</td>\n",
       "      <td>-0.007847</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>0.894599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>0.009117</td>\n",
       "      <td>-0.005176</td>\n",
       "      <td>-0.010343</td>\n",
       "      <td>0.000593</td>\n",
       "      <td>-0.001047</td>\n",
       "      <td>-0.001956</td>\n",
       "      <td>0.010198</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income_cat</th>\n",
       "      <td>0.002276</td>\n",
       "      <td>0.004711</td>\n",
       "      <td>0.000690</td>\n",
       "      <td>-0.000997</td>\n",
       "      <td>-0.005692</td>\n",
       "      <td>0.006368</td>\n",
       "      <td>-0.006634</td>\n",
       "      <td>0.894599</td>\n",
       "      <td>0.000588</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    longitude  latitude  housing_median_age  total_rooms  \\\n",
       "longitude            1.000000  0.000891           -0.004574     0.003629   \n",
       "latitude             0.000891  1.000000            0.009445     0.012716   \n",
       "housing_median_age  -0.004574  0.009445            1.000000    -0.005760   \n",
       "total_rooms          0.003629  0.012716           -0.005760     1.000000   \n",
       "total_bedrooms      -0.001897  0.000998           -0.002436    -0.005722   \n",
       "population          -0.006753 -0.012070           -0.003759     0.014791   \n",
       "households           0.004633  0.010274           -0.007078    -0.003304   \n",
       "median_income        0.001500  0.001776            0.002142    -0.000532   \n",
       "median_house_value   0.009117 -0.005176           -0.010343     0.000593   \n",
       "income_cat           0.002276  0.004711            0.000690    -0.000997   \n",
       "\n",
       "                    total_bedrooms  population  households  median_income  \\\n",
       "longitude                -0.001897   -0.006753    0.004633       0.001500   \n",
       "latitude                  0.000998   -0.012070    0.010274       0.001776   \n",
       "housing_median_age       -0.002436   -0.003759   -0.007078       0.002142   \n",
       "total_rooms              -0.005722    0.014791   -0.003304      -0.000532   \n",
       "total_bedrooms            1.000000   -0.002994    0.000655      -0.006225   \n",
       "population               -0.002994    1.000000    0.003583       0.010723   \n",
       "households                0.000655    0.003583    1.000000      -0.007847   \n",
       "median_income            -0.006225    0.010723   -0.007847       1.000000   \n",
       "median_house_value       -0.001047   -0.001956    0.010198       0.002932   \n",
       "income_cat               -0.005692    0.006368   -0.006634       0.894599   \n",
       "\n",
       "                    median_house_value  income_cat  \n",
       "longitude                     0.009117    0.002276  \n",
       "latitude                     -0.005176    0.004711  \n",
       "housing_median_age           -0.010343    0.000690  \n",
       "total_rooms                   0.000593   -0.000997  \n",
       "total_bedrooms               -0.001047   -0.005692  \n",
       "population                   -0.001956    0.006368  \n",
       "households                    0.010198   -0.006634  \n",
       "median_income                 0.002932    0.894599  \n",
       "median_house_value            1.000000    0.000588  \n",
       "income_cat                    0.000588    1.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# corr() 计算出每对特征之间的相关系数，称为皮尔逊相关系数\n",
    "corr_matrix= housing.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value    1.000000\n",
       "households            0.010198\n",
       "longitude             0.009117\n",
       "median_income         0.002932\n",
       "total_rooms           0.000593\n",
       "income_cat            0.000588\n",
       "total_bedrooms       -0.001047\n",
       "population           -0.001956\n",
       "latitude             -0.005176\n",
       "housing_median_age   -0.010343\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   * 相关系数范围从-1变化到1，越接近1，表示越强的正关系，比如，当收入中位数上升时，房价中位数也区域上升，\n",
    "   * 当系数接近-1，则表示有强烈的负相关，纬度和房价中位数之间出现轻微的负相关，越往北走，房价倾向于下降，\n",
    "   * 系数靠近0，说明二者之间没有线性相关，\n",
    "   * 相关系数仅检测线性相关性，如果x上升，y上升或者下降，可能会彻底遗漏非线性相关性，例如如果x接近于0，y上升"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-53-27569de102ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mattributes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'median_house_value'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'median_income'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'total_rooms'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'housing_median_age'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mscatter_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mattributes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
     ]
    }
   ],
   "source": [
    "#使用padans sctter_matrix函数，可以绘制出每个属性或特征之间的相关性\n",
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "attributes = ['median_house_value','median_income','total_rooms','housing_median_age']\n",
    "scatter_matrix(housing[attributes],figsize = (*12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 16, 0, 550000]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "housing.plot(kind = 'scatter',x='median_income',y='median_house_value',alpha = 0.1)\n",
    "plt.axis([0,16,0,550000])#改变xy轴长短大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#50万美元是一条清晰的线，被设置上限了，45w，35w，28w，也有虚线，可能是异常值，可以在后期把数据删除低掉"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 在给机器学习算法输入数据之前，我们识别出了一些异常数据，需要提前清理掉，也发现了不同属性之间的某些联系，跟目标属性相关的联系\n",
    "2. 某些属性分布显示出了明显的“重尾”分布，对进行转换处理，计算其对数\n",
    "3. 在给机器学习算法输入数据之前，最后一件事儿，尝试各种属性的组合\n",
    "4. 每个项目历程都不一样，大致思路相似"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 尝试不同特征组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>median_income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41</td>\n",
       "      <td>880</td>\n",
       "      <td>129</td>\n",
       "      <td>322</td>\n",
       "      <td>126</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21</td>\n",
       "      <td>7099</td>\n",
       "      <td>1106</td>\n",
       "      <td>2401</td>\n",
       "      <td>1138</td>\n",
       "      <td>8.3041</td>\n",
       "      <td>358500</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1467</td>\n",
       "      <td>190</td>\n",
       "      <td>496</td>\n",
       "      <td>177</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235</td>\n",
       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1627</td>\n",
       "      <td>280</td>\n",
       "      <td>565</td>\n",
       "      <td>259</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                  41          880             129   \n",
       "1    -122.22     37.86                  21         7099            1106   \n",
       "2    -122.24     37.85                  52         1467             190   \n",
       "3    -122.25     37.85                  52         1274             235   \n",
       "4    -122.25     37.85                  52         1627             280   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \\\n",
       "0         322         126         8.3252              452600        NEAR BAY   \n",
       "1        2401        1138         8.3041              358500        NEAR BAY   \n",
       "2         496         177         7.2574              352100        NEAR BAY   \n",
       "3         558         219         5.6431              341300        NEAR BAY   \n",
       "4         565         259         3.8462              342200        NEAR BAY   \n",
       "\n",
       "   income_cat median_income_cat  \n",
       "0         5.0                 5  \n",
       "1         5.0                 5  \n",
       "2         5.0                 5  \n",
       "3         4.0                 4  \n",
       "4         3.0                 3  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing['rooms_per_household'] = housing['total_rooms']/housing['households']\n",
    "housing['bedrooms_per_room'] = housing['total_bedrooms']/housing['total_rooms']\n",
    "housing['population_per_households'] = housing['population']/housing['households']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_house_value           1.000000\n",
       "households                   0.010198\n",
       "longitude                    0.009117\n",
       "median_income                0.002932\n",
       "total_rooms                  0.000593\n",
       "income_cat                   0.000588\n",
       "total_bedrooms              -0.001047\n",
       "population                  -0.001956\n",
       "latitude                    -0.005176\n",
       "bedrooms_per_room           -0.005419\n",
       "rooms_per_household         -0.006961\n",
       "population_per_households   -0.008274\n",
       "housing_median_age          -0.010343\n",
       "Name: median_house_value, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#关联矩阵\n",
    "corr_matrix = housing.corr()\n",
    "corr_matrix['median_house_value'].sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
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       "      <th>median_house_value</th>\n",
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       "      <th>median_income_cat</th>\n",
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       "      <th>12381</th>\n",
       "      <td>-122.245973</td>\n",
       "      <td>37.855985</td>\n",
       "      <td>27</td>\n",
       "      <td>2582</td>\n",
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       "      <td>146</td>\n",
       "      <td>8.075371</td>\n",
       "      <td>443344</td>\n",
       "      <td>NEAR BAY</td>\n",
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       "      <td>5</td>\n",
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       "      <td>8.219443</td>\n",
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       "      <td>NEAR OCEAN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
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       "      <td>363</td>\n",
       "      <td>477</td>\n",
       "      <td>7.758910</td>\n",
       "      <td>369946</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        longitude   latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "12381 -122.245973  37.855985                  27         2582             512   \n",
       "15153 -122.221184  37.873214                  47         2407             904   \n",
       "8891  -122.238933  37.877959                  43         3436             314   \n",
       "7020  -122.224909  37.862056                  44         1567             296   \n",
       "2832  -122.247946  37.856229                  29         5326             340   \n",
       "\n",
       "       population  households  median_income  median_house_value  \\\n",
       "12381         409         146       8.075371              443344   \n",
       "15153         345         776       8.219443              387409   \n",
       "8891          850         141       7.498699              392157   \n",
       "7020         2335         498       6.024719              387497   \n",
       "2832          363         477       7.758910              369946   \n",
       "\n",
       "      ocean_proximity  income_cat median_income_cat  \n",
       "12381        NEAR BAY         5.0                 5  \n",
       "15153      NEAR OCEAN         5.0                 5  \n",
       "8891       NEAR OCEAN         5.0                 5  \n",
       "7020       NEAR OCEAN         5.0                 5  \n",
       "2832           INLAND         5.0                 5  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#得到一个分层抽样代表全局数据集的 训练集和测试集\n",
    "strat_train_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据集的数据和标签分开\n",
    "housing_labels = strat_train_set['median_house_value'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "housing = strat_train_set.drop('median_house_value',axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
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       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
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       "      <td>363</td>\n",
       "      <td>477</td>\n",
       "      <td>7.758910</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        longitude   latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "12381 -122.245973  37.855985                  27         2582             512   \n",
       "15153 -122.221184  37.873214                  47         2407             904   \n",
       "8891  -122.238933  37.877959                  43         3436             314   \n",
       "7020  -122.224909  37.862056                  44         1567             296   \n",
       "2832  -122.247946  37.856229                  29         5326             340   \n",
       "\n",
       "       population  households  median_income ocean_proximity  income_cat  \\\n",
       "12381         409         146       8.075371        NEAR BAY         5.0   \n",
       "15153         345         776       8.219443      NEAR OCEAN         5.0   \n",
       "8891          850         141       7.498699      NEAR OCEAN         5.0   \n",
       "7020         2335         498       6.024719      NEAR OCEAN         5.0   \n",
       "2832          363         477       7.758910          INLAND         5.0   \n",
       "\n",
       "      median_income_cat  \n",
       "12381                 5  \n",
       "15153                 5  \n",
       "8891                  5  \n",
       "7020                  5  \n",
       "2832                  5  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 16512 entries, 12381 to 9872\n",
      "Data columns (total 11 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   longitude           16512 non-null  float64 \n",
      " 1   latitude            16512 non-null  float64 \n",
      " 2   housing_median_age  16512 non-null  int64   \n",
      " 3   total_rooms         16512 non-null  int64   \n",
      " 4   total_bedrooms      16512 non-null  int64   \n",
      " 5   population          16512 non-null  int64   \n",
      " 6   households          16512 non-null  int64   \n",
      " 7   median_income       16512 non-null  float64 \n",
      " 8   ocean_proximity     16512 non-null  object  \n",
      " 9   income_cat          16512 non-null  float64 \n",
      " 10  median_income_cat   16512 non-null  category\n",
      "dtypes: category(1), float64(4), int64(5), object(1)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "housing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 大多数机器学算法无法在缺失的特征上工作，创建一些函数辅助，total_bedrooms有缺失，\n",
    "* 放弃这些区域\n",
    "* 放弃这个属性\n",
    "* 将缺失值设置为某个值，（0，平均数或者中位数都可以）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>median_income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity, income_cat, median_income_cat]\n",
       "Index: []"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = housing[housing.isnull().any(axis = 1)] #返回整个数据集中位置的矩阵,取出返回值为null的数据\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'SimpleImputer' from 'sklearn.preprocessing' (/Users/lv/Downloads/Python/Practice/jupyter notebook practice/venv/lib/python3.7/site-packages/sklearn/preprocessing/__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-113-fa40840c47d5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m#创建一个 imputer实例，指定你要用属性中的中位数值替换该属性的缺失值, 每种机器学习的算法，其实也是一种估算器\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSimpleImputer\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mSimpleImputer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mimputer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSimpleImputer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstrategy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'median'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'SimpleImputer' from 'sklearn.preprocessing' (/Users/lv/Downloads/Python/Practice/jupyter notebook practice/venv/lib/python3.7/site-packages/sklearn/preprocessing/__init__.py)"
     ]
    }
   ],
   "source": [
    "#放弃这些区域\n",
    "#rows.dropna(subset = ['total_bedrooms'])\n",
    "#放弃这个属性\n",
    "#row.drop('total_bedrooms',axis = 1)\n",
    "#将缺失值设置为某个值，（0，平均数或者中位数都可以）\n",
    "\n",
    "#创建一个 imputer实例，指定你要用属性中的中位数值替换该属性的缺失值, 每种机器学习的算法，其实也是一种估算器\n",
    "\n",
    "from sklearn.preprocessing import Imputer as SimpleImputer\n",
    "\n",
    "imputer = SimpleImputer(strategy = 'median')\n",
    "\n",
    "#使用fit()方法将imputer实例适配到训练集\n",
    "housing_num = housing.drop('ocean_proximity',axis = 1)\n",
    "\n",
    "imputer.fit(housing_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Simpleimputer' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-114-06b703cfe6ee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mSimpleimputer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatistics_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'Simpleimputer' is not defined"
     ]
    }
   ],
   "source": [
    "Simpleimputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_num' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-69-8050cbb6f664>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing_num\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_num' is not defined"
     ]
    }
   ],
   "source": [
    "housing_num.median().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'imputer' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-70-0e8dd5e5c13f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimputer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'imputer' is not defined"
     ]
    }
   ],
   "source": [
    "X = imputer.transform(housing_num)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-71-1a46c9ac2d64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing_tr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhousing_num\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhousing\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mhousing_tr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X' is not defined"
     ]
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(X,columns = housing_num.columns,index = housing.index)\n",
    "housing_tr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>median_income_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population, households, median_income, ocean_proximity, income_cat, median_income_cat]\n",
       "Index: []"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = housing[housing.isnull().any(axis = 1)] #返回整个数据集中位置的矩阵,取出返回值为null的数据\n",
    "rows.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-Learn\n",
    " * 一致性\n",
    "     1. 估算器比如：各种机器学习算法fit()执行估算器\n",
    "     2. 转换器\n",
    "        比如：LabelBinarizer transform()执行转换数据集fit_transform()先估算，再转换\n",
    "     3. 预测器predict()对给定的新数据集进行预测score()评估测试集的预测质量\n",
    " * 检查\n",
    "     1. imputer.strategy_学习参数通过公共实例变量访问"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 处理文本和分类属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12381</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15153</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8891</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7020</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>INLAND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5632</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12574</th>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14976</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11532</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14274</th>\n",
       "      <td>NEAR OCEAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ocean_proximity\n",
       "12381        NEAR BAY\n",
       "15153      NEAR OCEAN\n",
       "8891       NEAR OCEAN\n",
       "7020       NEAR OCEAN\n",
       "2832           INLAND\n",
       "5632       NEAR OCEAN\n",
       "12574        NEAR BAY\n",
       "14976      NEAR OCEAN\n",
       "11532      NEAR OCEAN\n",
       "14274      NEAR OCEAN"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat = housing[['ocean_proximity']]\n",
    "housing_cat.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NEAR OCEAN    8475\n",
       "INLAND        4260\n",
       "NEAR BAY      3080\n",
       "<1H OCEAN      693\n",
       "ISLAND           4\n",
       "Name: ocean_proximity, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing['ocean_proximity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 4, ..., 4, 4, 4])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将文本转化为对应的数字分类，使用转化器\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "encoder = LabelEncoder()\n",
    "housing_cat = housing['ocean_proximity']\n",
    "housing_cat_encoder = encoder.fit_transform(housing_cat)\n",
    "housing_cat_encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['<1H OCEAN', 'INLAND', 'ISLAND', 'NEAR BAY', 'NEAR OCEAN'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.classes_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 机器学习算法会以为两个相近的数字比远的数字更相似，比如0，4比0，1相似度更高\n",
    "2. 创建独热编码，当<1H，第0个属性为1，其余都为0，列别是InLand时候，另一个属性为1，其余为0，1为热，0为冷，独热编码\n",
    "3. OneHotEncoder编码器，fit_trans需要二维数组，转换housing_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3],\n",
       "       [4],\n",
       "       [4],\n",
       "       ...,\n",
       "       [4],\n",
       "       [4],\n",
       "       [4]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_cat_encoder.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "fit_transform() missing 1 required positional argument: 'X'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-89-b31098128054>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing_cat_hot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_cat_encoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mhousing_cat_hot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: fit_transform() missing 1 required positional argument: 'X'"
     ]
    }
   ],
   "source": [
    "housing_cat_hot = encoder.fit_transform(housing_cat_encoder.reshape(-1, 1))\n",
    "housing_cat_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_cat_hot' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-90-3856477e1c06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#从稀疏矩阵，转成numpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhousing_cat_hot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_cat_hot' is not defined"
     ]
    }
   ],
   "source": [
    "#从稀疏矩阵，转成numpy\n",
    "housing_cat_hot.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 1, 0],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1],\n",
       "       [0, 0, 0, 0, 1]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#一次完成两个转换，文本--整数类型--独热类型\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer()\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<16512x5 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 16512 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#输出稀疏矩阵\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "encoder = LabelBinarizer(sparse_output = True)\n",
    "housing_cat_1hot = encoder.fit_transform(housing_cat)\n",
    "housing_cat_1hot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 自定义转换器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator,TransformerMixin\n",
    "\n",
    "rooms_ix,bedrooms_ix,population_ix,household_ix = [list(housing.columns).index(col) for col in(\"total_rooms\",\"total_bedrooms\",\"population\",\"households\")]\n",
    "\n",
    "class CombinedAttributesAdder(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,add_bedrooms_per_room = True):\n",
    "        self.add_bedrooms_per_room = add_bedrooms_per_room\n",
    "    def fit(self,X,y = None):\n",
    "        return self\n",
    "    def transform(self,X,y = None):\n",
    "        rooms_per_household = X[:,rooms_ix]/X[:,household_ix]\n",
    "        population_per_household = X[:,population_ix]/X[:,household_ix]\n",
    "        if self.add_bedrooms_per_room:\n",
    "            bedrooms_per_room = X[:,bedrooms_ix]/X[:,rooms_ix]\n",
    "            return np.c_[X,rooms_per_household,population_per_household,bedrooms_per_room]\n",
    "        else:\n",
    "            return np.c_[X,rooms_per_household,population_per_household]\n",
    "        \n",
    "attr_adder = CombinedAttributesAdder(add_bedrooms_per_room = False)\n",
    "housing_extra_attribs = attr_adder.transform(housing.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-122.245973059925, 37.8559846687462, 27, ..., 5,\n",
       "        17.684931506849313, 2.8013698630136985],\n",
       "       [-122.221183625411, 37.873214333833204, 47, ..., 5,\n",
       "        3.1018041237113403, 0.44458762886597936],\n",
       "       [-122.23893275469901, 37.8779590572226, 43, ..., 5,\n",
       "        24.368794326241133, 6.028368794326241],\n",
       "       ...,\n",
       "       [-122.223432900059, 37.871617626838706, 49, ..., 4,\n",
       "        2.3848238482384825, 1.1043360433604337],\n",
       "       [-122.233061802159, 37.8526387415867, 37, ..., 5,\n",
       "        5.118993135011442, 0.9794050343249427],\n",
       "       [-122.24524280154499, 37.873185929926294, 39, ..., 5,\n",
       "        7.921182266009852, 2.9679802955665027]], dtype=object)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of              0        1   2     3    4     5    6        7           8  9 10  \\\n",
       "0     -122.246   37.856  27  2582  512   409  146  8.07537    NEAR BAY  5  5   \n",
       "1     -122.221  37.8732  47  2407  904   345  776  8.21944  NEAR OCEAN  5  5   \n",
       "2     -122.239   37.878  43  3436  314   850  141   7.4987  NEAR OCEAN  5  5   \n",
       "3     -122.225  37.8621  44  1567  296  2335  498  6.02472  NEAR OCEAN  5  5   \n",
       "4     -122.248  37.8562  29  5326  340   363  477  7.75891      INLAND  5  5   \n",
       "...        ...      ...  ..   ...  ...   ...  ...      ...         ... .. ..   \n",
       "16507 -122.239  37.8658  45  1428  474  1143  935  7.71852  NEAR OCEAN  5  5   \n",
       "16508  -122.25  37.8693  25  4155  646   966  330   6.9563  NEAR OCEAN  5  5   \n",
       "16509 -122.223  37.8716  49  1760  650   815  738  5.25568  NEAR OCEAN  4  4   \n",
       "16510 -122.233  37.8526  37  2237  680   428  437  6.29652  NEAR OCEAN  5  5   \n",
       "16511 -122.245  37.8732  39  3216  854  1205  406  7.77129  NEAR OCEAN  5  5   \n",
       "\n",
       "            11        12  \n",
       "0      17.6849   2.80137  \n",
       "1       3.1018  0.444588  \n",
       "2      24.3688   6.02837  \n",
       "3      3.14659   4.68876  \n",
       "4      11.1656  0.761006  \n",
       "...        ...       ...  \n",
       "16507  1.52727   1.22246  \n",
       "16508  12.5909   2.92727  \n",
       "16509  2.38482   1.10434  \n",
       "16510  5.11899  0.979405  \n",
       "16511  7.92118   2.96798  \n",
       "\n",
       "[16512 rows x 13 columns]>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_tr = pd.DataFrame(housing_extra_attribs)\n",
    "housing_tr.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>median_income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12381</th>\n",
       "      <td>-122.246</td>\n",
       "      <td>37.856</td>\n",
       "      <td>27</td>\n",
       "      <td>2582</td>\n",
       "      <td>512</td>\n",
       "      <td>409</td>\n",
       "      <td>146</td>\n",
       "      <td>8.07537</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>17.6849</td>\n",
       "      <td>2.80137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15153</th>\n",
       "      <td>-122.221</td>\n",
       "      <td>37.8732</td>\n",
       "      <td>47</td>\n",
       "      <td>2407</td>\n",
       "      <td>904</td>\n",
       "      <td>345</td>\n",
       "      <td>776</td>\n",
       "      <td>8.21944</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3.1018</td>\n",
       "      <td>0.444588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8891</th>\n",
       "      <td>-122.239</td>\n",
       "      <td>37.878</td>\n",
       "      <td>43</td>\n",
       "      <td>3436</td>\n",
       "      <td>314</td>\n",
       "      <td>850</td>\n",
       "      <td>141</td>\n",
       "      <td>7.4987</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>24.3688</td>\n",
       "      <td>6.02837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7020</th>\n",
       "      <td>-122.225</td>\n",
       "      <td>37.8621</td>\n",
       "      <td>44</td>\n",
       "      <td>1567</td>\n",
       "      <td>296</td>\n",
       "      <td>2335</td>\n",
       "      <td>498</td>\n",
       "      <td>6.02472</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3.14659</td>\n",
       "      <td>4.68876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>-122.248</td>\n",
       "      <td>37.8562</td>\n",
       "      <td>29</td>\n",
       "      <td>5326</td>\n",
       "      <td>340</td>\n",
       "      <td>363</td>\n",
       "      <td>477</td>\n",
       "      <td>7.75891</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>11.1656</td>\n",
       "      <td>0.761006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "12381  -122.246   37.856                 27        2582            512   \n",
       "15153  -122.221  37.8732                 47        2407            904   \n",
       "8891   -122.239   37.878                 43        3436            314   \n",
       "7020   -122.225  37.8621                 44        1567            296   \n",
       "2832   -122.248  37.8562                 29        5326            340   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "12381        409        146       8.07537        NEAR BAY          5   \n",
       "15153        345        776       8.21944      NEAR OCEAN          5   \n",
       "8891         850        141        7.4987      NEAR OCEAN          5   \n",
       "7020        2335        498       6.02472      NEAR OCEAN          5   \n",
       "2832         363        477       7.75891          INLAND          5   \n",
       "\n",
       "      median_income_cat rooms_per_household population_per_household  \n",
       "12381                 5             17.6849                  2.80137  \n",
       "15153                 5              3.1018                 0.444588  \n",
       "8891                  5             24.3688                  6.02837  \n",
       "7020                  5             3.14659                  4.68876  \n",
       "2832                  5             11.1656                 0.761006  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs = pd.DataFrame(\n",
    "    housing_extra_attribs,\n",
    "    columns = list(housing.columns)+[\"rooms_per_household\",\"population_per_household\"],\n",
    "    index=housing.index)\n",
    "housing_extra_attribs.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>income_cat</th>\n",
       "      <th>median_income_cat</th>\n",
       "      <th>rooms_per_household</th>\n",
       "      <th>population_per_household</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5620</th>\n",
       "      <td>-122.242</td>\n",
       "      <td>37.8577</td>\n",
       "      <td>47</td>\n",
       "      <td>2489</td>\n",
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       "      <td>1390</td>\n",
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       "      <td>4.72277</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7.25656</td>\n",
       "      <td>4.05248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18229</th>\n",
       "      <td>-122.237</td>\n",
       "      <td>37.8572</td>\n",
       "      <td>21</td>\n",
       "      <td>1165</td>\n",
       "      <td>134</td>\n",
       "      <td>958</td>\n",
       "      <td>699</td>\n",
       "      <td>5.09788</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.66667</td>\n",
       "      <td>1.37053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10860</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.8573</td>\n",
       "      <td>38</td>\n",
       "      <td>1141</td>\n",
       "      <td>434</td>\n",
       "      <td>1134</td>\n",
       "      <td>927</td>\n",
       "      <td>4.35128</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1.23085</td>\n",
       "      <td>1.2233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6149</th>\n",
       "      <td>-122.239</td>\n",
       "      <td>37.8662</td>\n",
       "      <td>31</td>\n",
       "      <td>5265</td>\n",
       "      <td>579</td>\n",
       "      <td>2041</td>\n",
       "      <td>624</td>\n",
       "      <td>7.15667</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8.4375</td>\n",
       "      <td>3.27083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8153</th>\n",
       "      <td>-122.237</td>\n",
       "      <td>37.8616</td>\n",
       "      <td>21</td>\n",
       "      <td>2010</td>\n",
       "      <td>303</td>\n",
       "      <td>1921</td>\n",
       "      <td>301</td>\n",
       "      <td>6.79729</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6.67774</td>\n",
       "      <td>6.38206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6041</th>\n",
       "      <td>-122.236</td>\n",
       "      <td>37.8676</td>\n",
       "      <td>39</td>\n",
       "      <td>5063</td>\n",
       "      <td>1101</td>\n",
       "      <td>688</td>\n",
       "      <td>820</td>\n",
       "      <td>7.11468</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6.17439</td>\n",
       "      <td>0.839024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18284</th>\n",
       "      <td>-122.228</td>\n",
       "      <td>37.8593</td>\n",
       "      <td>32</td>\n",
       "      <td>6670</td>\n",
       "      <td>975</td>\n",
       "      <td>835</td>\n",
       "      <td>171</td>\n",
       "      <td>4.02027</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>39.0058</td>\n",
       "      <td>4.88304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4905</th>\n",
       "      <td>-122.243</td>\n",
       "      <td>37.8549</td>\n",
       "      <td>37</td>\n",
       "      <td>963</td>\n",
       "      <td>681</td>\n",
       "      <td>369</td>\n",
       "      <td>281</td>\n",
       "      <td>5.31319</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3.42705</td>\n",
       "      <td>1.31317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14552</th>\n",
       "      <td>-122.245</td>\n",
       "      <td>37.8542</td>\n",
       "      <td>37</td>\n",
       "      <td>2144</td>\n",
       "      <td>857</td>\n",
       "      <td>1257</td>\n",
       "      <td>169</td>\n",
       "      <td>7.54851</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>12.6864</td>\n",
       "      <td>7.43787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>-122.237</td>\n",
       "      <td>37.854</td>\n",
       "      <td>29</td>\n",
       "      <td>1492</td>\n",
       "      <td>706</td>\n",
       "      <td>2305</td>\n",
       "      <td>184</td>\n",
       "      <td>5.18011</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8.1087</td>\n",
       "      <td>12.5272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15034</th>\n",
       "      <td>-122.242</td>\n",
       "      <td>37.8773</td>\n",
       "      <td>50</td>\n",
       "      <td>2518</td>\n",
       "      <td>201</td>\n",
       "      <td>1866</td>\n",
       "      <td>171</td>\n",
       "      <td>5.71623</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>14.7251</td>\n",
       "      <td>10.9123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7572</th>\n",
       "      <td>-122.221</td>\n",
       "      <td>37.8526</td>\n",
       "      <td>24</td>\n",
       "      <td>1030</td>\n",
       "      <td>601</td>\n",
       "      <td>1157</td>\n",
       "      <td>459</td>\n",
       "      <td>6.52392</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2.24401</td>\n",
       "      <td>2.5207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11548</th>\n",
       "      <td>-122.229</td>\n",
       "      <td>37.8508</td>\n",
       "      <td>34</td>\n",
       "      <td>6013</td>\n",
       "      <td>1038</td>\n",
       "      <td>412</td>\n",
       "      <td>678</td>\n",
       "      <td>6.42725</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8.86873</td>\n",
       "      <td>0.60767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3557</th>\n",
       "      <td>-122.229</td>\n",
       "      <td>37.873</td>\n",
       "      <td>49</td>\n",
       "      <td>6917</td>\n",
       "      <td>392</td>\n",
       "      <td>1643</td>\n",
       "      <td>580</td>\n",
       "      <td>7.59641</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>11.9259</td>\n",
       "      <td>2.83276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7143</th>\n",
       "      <td>-122.223</td>\n",
       "      <td>37.8569</td>\n",
       "      <td>31</td>\n",
       "      <td>1567</td>\n",
       "      <td>1066</td>\n",
       "      <td>1324</td>\n",
       "      <td>784</td>\n",
       "      <td>5.41252</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.99872</td>\n",
       "      <td>1.68878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9179</th>\n",
       "      <td>-122.238</td>\n",
       "      <td>37.8585</td>\n",
       "      <td>36</td>\n",
       "      <td>6978</td>\n",
       "      <td>560</td>\n",
       "      <td>1208</td>\n",
       "      <td>924</td>\n",
       "      <td>4.06056</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7.55195</td>\n",
       "      <td>1.30736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5066</th>\n",
       "      <td>-122.246</td>\n",
       "      <td>37.8652</td>\n",
       "      <td>34</td>\n",
       "      <td>3337</td>\n",
       "      <td>202</td>\n",
       "      <td>2119</td>\n",
       "      <td>203</td>\n",
       "      <td>5.65635</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>16.4384</td>\n",
       "      <td>10.4384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>-122.227</td>\n",
       "      <td>37.8553</td>\n",
       "      <td>41</td>\n",
       "      <td>4382</td>\n",
       "      <td>694</td>\n",
       "      <td>819</td>\n",
       "      <td>1013</td>\n",
       "      <td>6.06217</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4.32577</td>\n",
       "      <td>0.80849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14731</th>\n",
       "      <td>-122.229</td>\n",
       "      <td>37.8786</td>\n",
       "      <td>27</td>\n",
       "      <td>3448</td>\n",
       "      <td>353</td>\n",
       "      <td>1866</td>\n",
       "      <td>290</td>\n",
       "      <td>6.36377</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>11.8897</td>\n",
       "      <td>6.43448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14782</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.8761</td>\n",
       "      <td>33</td>\n",
       "      <td>4284</td>\n",
       "      <td>336</td>\n",
       "      <td>1029</td>\n",
       "      <td>597</td>\n",
       "      <td>6.2877</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>7.17588</td>\n",
       "      <td>1.72362</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude latitude housing_median_age total_rooms total_bedrooms  \\\n",
       "5620   -122.242  37.8577                 47        2489            393   \n",
       "18229  -122.237  37.8572                 21        1165            134   \n",
       "10860   -122.25  37.8573                 38        1141            434   \n",
       "6149   -122.239  37.8662                 31        5265            579   \n",
       "8153   -122.237  37.8616                 21        2010            303   \n",
       "6041   -122.236  37.8676                 39        5063           1101   \n",
       "18284  -122.228  37.8593                 32        6670            975   \n",
       "4905   -122.243  37.8549                 37         963            681   \n",
       "14552  -122.245  37.8542                 37        2144            857   \n",
       "771    -122.237   37.854                 29        1492            706   \n",
       "15034  -122.242  37.8773                 50        2518            201   \n",
       "7572   -122.221  37.8526                 24        1030            601   \n",
       "11548  -122.229  37.8508                 34        6013           1038   \n",
       "3557   -122.229   37.873                 49        6917            392   \n",
       "7143   -122.223  37.8569                 31        1567           1066   \n",
       "9179   -122.238  37.8585                 36        6978            560   \n",
       "5066   -122.246  37.8652                 34        3337            202   \n",
       "2514   -122.227  37.8553                 41        4382            694   \n",
       "14731  -122.229  37.8786                 27        3448            353   \n",
       "14782   -122.24  37.8761                 33        4284            336   \n",
       "\n",
       "      population households median_income ocean_proximity income_cat  \\\n",
       "5620        1390        343       4.72277          INLAND          4   \n",
       "18229        958        699       5.09788       <1H OCEAN          4   \n",
       "10860       1134        927       4.35128        NEAR BAY          3   \n",
       "6149        2041        624       7.15667      NEAR OCEAN          5   \n",
       "8153        1921        301       6.79729      NEAR OCEAN          5   \n",
       "6041         688        820       7.11468      NEAR OCEAN          5   \n",
       "18284        835        171       4.02027       <1H OCEAN          3   \n",
       "4905         369        281       5.31319          INLAND          4   \n",
       "14552       1257        169       7.54851      NEAR OCEAN          5   \n",
       "771         2305        184       5.18011          INLAND          4   \n",
       "15034       1866        171       5.71623      NEAR OCEAN          4   \n",
       "7572        1157        459       6.52392      NEAR OCEAN          5   \n",
       "11548        412        678       6.42725      NEAR OCEAN          5   \n",
       "3557        1643        580       7.59641        NEAR BAY          5   \n",
       "7143        1324        784       5.41252      NEAR OCEAN          4   \n",
       "9179        1208        924       4.06056      NEAR OCEAN          3   \n",
       "5066        2119        203       5.65635          INLAND          4   \n",
       "2514         819       1013       6.06217          INLAND          5   \n",
       "14731       1866        290       6.36377      NEAR OCEAN          5   \n",
       "14782       1029        597        6.2877      NEAR OCEAN          5   \n",
       "\n",
       "      median_income_cat rooms_per_household population_per_household  \n",
       "5620                  4             7.25656                  4.05248  \n",
       "18229                 4             1.66667                  1.37053  \n",
       "10860                 3             1.23085                   1.2233  \n",
       "6149                  5              8.4375                  3.27083  \n",
       "8153                  5             6.67774                  6.38206  \n",
       "6041                  5             6.17439                 0.839024  \n",
       "18284                 3             39.0058                  4.88304  \n",
       "4905                  4             3.42705                  1.31317  \n",
       "14552                 5             12.6864                  7.43787  \n",
       "771                   4              8.1087                  12.5272  \n",
       "15034                 4             14.7251                  10.9123  \n",
       "7572                  5             2.24401                   2.5207  \n",
       "11548                 5             8.86873                  0.60767  \n",
       "3557                  5             11.9259                  2.83276  \n",
       "7143                  4             1.99872                  1.68878  \n",
       "9179                  3             7.55195                  1.30736  \n",
       "5066                  4             16.4384                  10.4384  \n",
       "2514                  5             4.32577                  0.80849  \n",
       "14731                 5             11.8897                  6.43448  \n",
       "14782                 5             7.17588                  1.72362  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "housing_extra_attribs.sample(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征缩放 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 最重要也是最需要应用在数据上的转换器，就是特征缩放，输入数值属性有很大的比例差异，会导致机器学习算法性能表现不佳\n",
    "# 房价总数 范围6到39320，收入中位数范围0到15\n",
    "# 目标值通常不需要缩放\n",
    "# 同比缩放所有属性，2种方法 最小-最大缩放和标准化\n",
    "# 最小-最大缩放，又叫归一化，将值重新缩放到0到1之间，将值减去最小值并除以最大值和最小值差，如果你不希望是0到1，可以调整超参数featurerange\n",
    "# 标准化 减去平均值，所有标准化均值总是0，然后除以方差，结果的分布具备单位方差，不同于归一化，标准化不将值绑定到特定范围，受异常值影响小\n",
    "# 缩放器仅用来拟合训练集，不是完成的数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 转换流水线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Imputer' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-115-842a4428a897>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m num_pipeline = Pipeline([\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0;34m(\u001b[0m\u001b[0;34m'imputer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mImputer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstrategy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"median\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'attribs_adder'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mCombinedAttributesAdder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'std_scaler'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Imputer' is not defined"
     ]
    }
   ],
   "source": [
    "# 许多数据转换的步骤需要以正确的顺序来执行，pipeline来支持这样的转换\n",
    "# pipline构造函数会通过一系列名称/估算器的配对来定义步骤的序列，必须是转换器，必须有fit_transform()方法\n",
    "# 调用流水线的fit方法时，会在所有转换器上按找顺序依次调用fit_transform（），将一个调用的输出作为参互传递给下一个调用方法，直到传递到最终\n",
    "#估算器，只会调用fit方法\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('imputer',Imputer(strategy=\"median\")),\n",
    "    ('attribs_adder',CombinedAttributesAdder()),\n",
    "    ('std_scaler',StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_num' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-116-91bc8eb0a622>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing_num\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_num' is not defined"
     ]
    }
   ],
   "source": [
    "housing_num.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataFrame->series->ndarray\n",
    "class DataFrameSelector(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,attribute_names):\n",
    "        self.attribute_names = attribute_names\n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "    def transform(self,X):\n",
    "        return X[self.attribute_names].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_num' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-110-7d46567e934d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnum_attribs\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_num\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mnum_attribs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_num' is not defined"
     ]
    }
   ],
   "source": [
    "num_attribs =list(housing_num)\n",
    "num_attribs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'num_attribs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-117-07eaf7b7e995>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m num_pipeline = Pipeline([\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;34m(\u001b[0m\u001b[0;34m'selector'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mDataFrameSelector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_attribs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'imputer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mSimpleImputer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstrategy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"median\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'attribs_adder'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mCombinedAttributesAdder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'std_scaler'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'num_attribs' is not defined"
     ]
    }
   ],
   "source": [
    "num_pipeline = Pipeline([\n",
    "    ('selector',DataFrameSelector(num_attribs)),\n",
    "    ('imputer',SimpleImputer(strategy = \"median\")),\n",
    "    ('attribs_adder',CombinedAttributesAdder()),\n",
    "    ('std_scaler',StandardScaler()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "class MyLabelBinarizer(TransformerMixin):\n",
    "    def __init__(self,*args,**kwargs):\n",
    "        self.encoder = LabelBinarizer(*args,**kwargs)\n",
    "    def fit(self,x,y=0):\n",
    "        self.encoder.fit(x)\n",
    "        return self\n",
    "    def transform(self,x,y=0):\n",
    "        return self.encoder.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_attribs = ['ocean_proximity']\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "cat_pipeline = Pipeline([\n",
    "    ('selector',DataFrameSelector(cat_attribs)),\n",
    "    ('LabelBinarizer',MyLabelBinarizer()),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'num_pipeline' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-122-1f9dae6eab6b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m full_pipeline = FeatureUnion(transformer_list=[\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;34m(\u001b[0m\u001b[0;34m\"num_pipline\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum_pipeline\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'cat_pipline'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcat_pipeline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m ])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'num_pipeline' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.pipeline import FeatureUnion\n",
    "\n",
    "full_pipeline = FeatureUnion(transformer_list=[\n",
    "    (\"num_pipline\",num_pipeline,),\n",
    "    ('cat_pipline',cat_pipeline),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'full_pipeline' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-123-3d76f3655877>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing_finshed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfull_pipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mhousing_finshed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'full_pipeline' is not defined"
     ]
    }
   ],
   "source": [
    "housing_finshed = full_pipeline.fit_transform(housing)\n",
    "housing_finshed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_finshed' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-124-c2cedbe3c5db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing_finally\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_finshed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mhousing_finally\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_finshed' is not defined"
     ]
    }
   ],
   "source": [
    "housing_prepared = pd.DataFrame(housing_finshed)\n",
    "housing_prepared.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 选择和训练模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 获得了数据\n",
    "* 数据探索\n",
    "* 对训练集和测试集进行拆分\n",
    "* 编写了转换数据流水线\n",
    "* 自动清理和准备机器学习算法的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'DataFrame' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-125-742ed4fdfff0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_prepared\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mhousing_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'DataFrame' is not defined"
     ]
    }
   ],
   "source": [
    "df = DataFrame(housing_prepared)\n",
    "df.head()\n",
    "housing_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 训练模型和评估训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_prepared' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-127-d3c02111e0f9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mlin_reg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mlin_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_prepared\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhousing_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_prepared' is not defined"
     ]
    }
   ],
   "source": [
    "#线性模型\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(housing_prepared,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "#预测数据\n",
    "some_data = housing.iloc[:5]\n",
    "some_labels = housing_labels.iloc[:5]\n",
    "\n",
    "some_data_prepared = full_pipeline.transform(some_data)\n",
    "\n",
    "print(\"Predictions:\",lin_reg.predict(some_data_prepared))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_prepared' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-129-f697cdc12c62>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mhousing_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlin_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_prepared\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mlin_mse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhousing_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mlin_mse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_prepared' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "housing_predictions = lin_reg.predict(housing_prepared)\n",
    "lin_mse = mean_squared_error(housing_labels,housing_predictions)\n",
    "lin_mse\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse\n",
    "\n",
    "#大多数地区的房屋中位数 在120000到265000美元之间，预测误差高达68628，这是一个典型的模型对训练数据你和不足的案例，\n",
    "#原因可能是特征无法提供足够的信息来做出更好的预测，或者模型本身不够强大\n",
    "#1.选择强大的模型，2.为算法提供更好的特征， 3.减少对模型的限制等方法，\n",
    "# 决策树可以找到复杂的非线性关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_prepared' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-130-da4d2b8bee64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtree_reg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtree_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_prepared\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhousing_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_prepared' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "tree_reg = DecisionTreeRegressor(random_state = 42)\n",
    "tree_reg.fit(housing_prepared,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_prepared' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-131-2a1817519ed7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing_predictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_prepared\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtree_mse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhousing_predictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtree_rmse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtree_mse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtree_rmse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_prepared' is not defined"
     ]
    }
   ],
   "source": [
    "housing_predictions = tree_reg.predict(housing_prepared)\n",
    "tree_mse = mean_squared_error(housing_labels,housing_predictions)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "#完美，也可能是这个模型对数据严重过度拟合了，如何确认？轻易不要启用测试集，拿训练集中的一部分用于训练，另一部分用于模型的验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用交叉验证来更好的进行评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_prepared' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-134-aa68b6444332>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#neg_mean_squared_error' 也就是均方差回归损失 该统计参数是预测数据和原始数据对应点误差的平方和的均值\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m scores = cross_val_score(tree_reg,housing_prepared,housing_labels,\n\u001b[0m\u001b[1;32m      7\u001b[0m                         scoring=\"neg_mean_squared_error\",cv=10)\n\u001b[1;32m      8\u001b[0m \u001b[0mtree_rmse_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_prepared' is not defined"
     ]
    }
   ],
   "source": [
    "# 使用train_test_split函数将训练集分成较小的训练集和验证集，然后根据这些较小的训练集来训练模型，并对其进行评估\n",
    "# sklearn的交叉验证，将训练集随机分割成10个不同的子集，每个子集称为一个折叠，对模型进行10次训练和评估，每次挑选1个折叠进行评估，另外9个进行训练\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#neg_mean_squared_error' 也就是均方差回归损失 该统计参数是预测数据和原始数据对应点误差的平方和的均值\n",
    "\n",
    "scores = cross_val_score(tree_reg,housing_prepared,housing_labels,\n",
    "                        scoring=\"neg_mean_squared_error\",cv=10)\n",
    "#决策树\n",
    "tree_rmse_scores = np.sqrt(-scores)\n",
    "tree_rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tree_rmse_scores' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-135-e02af4cb5175>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Standard deviation:\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdisplay_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtree_rmse_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'tree_rmse_scores' is not defined"
     ]
    }
   ],
   "source": [
    "def display_scores(scores):\n",
    "    print(\"Scoress:\",scores)\n",
    "    print(\"Mean:\",scores.mean())\n",
    "    print(\"Standard deviation:\",scores.std())\n",
    "    \n",
    "display_scores(tree_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_prepared' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-136-075ffc7c9fd9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#线性 交叉验证\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m lin_scores = cross_val_score(lin_reg,housing_prepared,housing_labels,\n\u001b[0m\u001b[1;32m      3\u001b[0m                             scoring=\"neg_mean_squared_error\",cv = 10)\n\u001b[1;32m      4\u001b[0m \u001b[0mlin_rmse_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlin_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdisplay_scores\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlin_rmse_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_prepared' is not defined"
     ]
    }
   ],
   "source": [
    "#线性 交叉验证\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "lin_scores = cross_val_score(lin_reg,housing_prepared,housing_labels,\n",
    "                            scoring=\"neg_mean_squared_error\",cv = 10)\n",
    "lin_rmse_scores = np.sqrt(-lin_scores)\n",
    "display_scores(lin_rmse_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_prepared' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-137-ecdf1ef720f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mforest_reg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mforest_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_prepared\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhousing_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_prepared' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "forest_reg = RandomForestRegressor(n_estimators=10,random_state=42)\n",
    "forest_reg.fit(housing_prepared,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_prepared' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-138-9bc75c330356>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhousing_preditions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mforest_reg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_prepared\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mforest_mse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_prepared' is not defined"
     ]
    }
   ],
   "source": [
    "housing_preditions = forest_reg.predict(housing_prepared)\n",
    "forest_mse = mean_squared_error(housing_labels,housing_predictions)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_prepared' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-139-6503d111e61a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m lin_scores = cross_val_score(forest_reg,housing_prepared,housing_labels,\n\u001b[0m\u001b[1;32m      4\u001b[0m                             scoring=\"neg_mean_squared_error\",cv = 10)\n\u001b[1;32m      5\u001b[0m \u001b[0mlin_rmse_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mforest_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_prepared' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "lin_scores = cross_val_score(forest_reg,housing_prepared,housing_labels,\n",
    "                            scoring=\"neg_mean_squared_error\",cv = 10)\n",
    "lin_rmse_scores = np.sqrt(-forest_scores)\n",
    "display_scores(forest_rmse_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型调参和网格搜索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 手动调整超参数，找到很好的组合很困难\n",
    "2. 使用GridSearchCV替你进行搜索，告诉它，进行试验的超参数是什么，和需要尝试的值，它会使用交叉验证评估所有超参数的可能组合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'housing_prepared' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-140-a95197c24033>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m grid_search = GridSearchCV(forest_reg,param_grid,cv=5,\n\u001b[1;32m     10\u001b[0m                           scoring = 'neg_mean_squared_error',return_train_score=True)\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing_prepared\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhousing_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'housing_prepared' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid= [\n",
    "    {'n_estimators':[3,10,30],'max_features':[2,4,6,8]},\n",
    "    {'bootstrap':[False],'n_estimators':[3,10],'max_features':[2,3,4]},\n",
    "]\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "grid_search = GridSearchCV(forest_reg,param_grid,cv=5,\n",
    "                          scoring = 'neg_mean_squared_error',return_train_score=True)\n",
    "grid_search.fit(housing_prepared,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'best_params_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-141-b1068600e498>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_params_'"
     ]
    }
   ],
   "source": [
    "grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'best_estimator_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-142-95527c57fcf9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_estimator_'"
     ]
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'cv_results_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-143-2246773e4def>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcvres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcv_results_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmean_score\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mparams\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcvres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"mean_test_score\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcvres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"params\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mmean_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'cv_results_'"
     ]
    }
   ],
   "source": [
    "cvres = grid_search.cv_results_\n",
    "for mean_score,params in zip(cvres[\"mean_test_score\"],cvres[\"params\"]):\n",
    "    print(np.sqrt(-mean_score),params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据准备步骤也可以当作超参数来处理，网格搜索会自动查找是否添加你不确定的特征，比如是否使用转换器combinedAttre的超参数add_bedrooms_per_rom\n",
    "#也可是使用它自动寻找处理问题的最佳方法，比如异常值，缺失特征，特征选择等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机搜索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-146-b6398178dd8c>, line 9)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-146-b6398178dd8c>\"\u001b[0;36m, line \u001b[0;32m9\u001b[0m\n\u001b[0;31m    forest_reg = RandomForestRegressor(random_state=42)\u001b[0m\n\u001b[0m             ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import randint\n",
    "\n",
    "param_distribs= {\n",
    "    {'n_estimators':randint(low=1,high=200),\n",
    "    {'max_features':randint(low=1,high=8),\n",
    "}\n",
    "\n",
    "forest_reg = RandomForestRegressor(random_state=42)\n",
    "grid_search = RandomizedSearchCV(forest_reg,param_distributions=param_distribs,\n",
    "                          n_iter=10,cv=5,scoring='neg_mean_squared_error',random_state=42)\n",
    "rnd_search.fit(housing_prepared,housing_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分析最佳模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'best_estimator_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-148-4f49271ba39f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfeature_importances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_estimator_'"
     ]
    }
   ],
   "source": [
    "feature_importances = grid_search.best_estimator_.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'num_attribs' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-149-9648b61b802d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnum_attribs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'num_attribs' is not defined"
     ]
    }
   ],
   "source": [
    "# num_attribs\n",
    "extra_attribs = [\"rooms_household\",\"population_per_household\",\"bedrooms_per_room\"]\n",
    "cat_one_hot_attribs = list(encoder.classes_)\n",
    "attributes = num_attribs + extra_attribs + cat_one_hot_attribs\n",
    "sorted(zip(feature_importances,attributes),reverse = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 通过测试集评估系统"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 从测试集中获取预测器和标签\n",
    "2. 运行full_pipline来转换数据\n",
    "3. 在测试集上评估最终模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model = grid_search.best_estimator_\n",
    "\n",
    "X_test = strat_test_set.drop('median_house_value',axis = 1)\n",
    "y_test = strat_test_set['median_house_value'].copy()\n",
    "\n",
    "X_test_prepared = full_pipeline.transform(X_test)\n",
    "\n",
    "df = pd.DataFrame(X_test_prepared)\n",
    "\n",
    "final_predictions = final_model.predict(X_test_prepared)\n",
    "\n",
    "final_mse = mean_squared_error(y_test,final_predictions)\n",
    "final_rmse = np.sqrt(final_mse)\n",
    "final_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 项目启动阶段\n",
    "\n",
    "    1. 展示解决方案，学习了什么\n",
    "    2. 什么有用\n",
    "    3. 什么没有用\n",
    "    4. 基于假设\n",
    "    5. 以及系统的限制有那些\n",
    "    6. 制作漂亮的掩饰文档，例如收入中位数是预测房价的首要指标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 启动，监控和维护系统\n",
    "    1. 为生产环境做好准备，将生产数据源接入系统\n",
    "    2. 编写监控代码，定期检查系统的实时性能，性能下降时触发警报，系统崩溃和性能退化\n",
    "    3. 时间推移，模型会渐渐腐坏，定期使用新数据训练模型\n",
    "    \n",
    "# 评估系统性能\n",
    "    1. 需要对系统的预测结果进行抽样评估，需要人工分析，分析师可以是专家，平台工作人员，都需要将人工评估的流水线接入你的系统\n",
    "    2. 评估输入系统的数据质量\n",
    "    3. 使用新鲜数据定期训练你的模型，最多6个月"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结\n",
    "    本周主要学习 数据准备，构建监控工作，建立人工评估流水线，自动化定期训练模型上，熟悉整个机器学习流程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "网址：http://www.kaggle.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第二题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 7 6 0 3 2 5 4]\n",
      "[2. 0. 1. 3. 4. 5. 6. 7.]\n",
      "[2. 0. 1. 3.]\n"
     ]
    }
   ],
   "source": [
    "# 在快排算法中，有一个典型的操作，partition，这个操作指：根除一个数值x，把数组中的元素划分成两半，使得index前面的元素都不大于x，\n",
    "# index后面的元素都不小于x\n",
    "\n",
    "dists = np.array([3.0,2.0,5.0,4.0,7.0,6.0,1.0,0.0])\n",
    "\n",
    "k = 5\n",
    "print(np.argpartition(dists,k))\n",
    "\n",
    "print(dists[np.argpartition(dists,k)])\n",
    "\n",
    "#取x个比K名次还高的\n",
    "X = 4\n",
    "print(dists[np.argpartition(dists,k)[:X]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'GridSearchCV' object has no attribute 'best_estimator_'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-151-cd2fd527e08d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m#[(1,2,3),(4,5,6)]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mfeature_importances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mba\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_importances\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mattributes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mba\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_estimator_'"
     ]
    }
   ],
   "source": [
    "# >>>a = [1,2,3]\n",
    "# >>>b = [4,5,6]\n",
    "# >>>c = [4,5,6,7,8]\n",
    "# >>>zipped = zip(a,b)   #打包为元祖的列表\n",
    "# >>>[(1,4),(2,5),(3,6)]\n",
    "# >>>zip(a,c)\n",
    "# >>>[(1,4),(2,5),(3,6)]\n",
    "# >>>zip(*zipped)\n",
    "#[(1,2,3),(4,5,6)]\n",
    "\n",
    "feature_importances = grid_search.best_estimator_.feature_importances_\n",
    "ba = sorted(zip(feature_importances,attributes))\n",
    "print(np.array(ba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator,TransformerMixin\n",
    "\n",
    "def indices_of_top_k(arr,k):\n",
    "    return np.sort(np.argpartition(np.array(arr),-k)[-k:])\n",
    "\n",
    "class TopFeatureSelector(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,feature_importances,k):\n",
    "        self.feature_importances = feature_importances\n",
    "        self.k = k\n",
    "    def fit(self,X,y = None):\n",
    "        self.feature_indices_ = indices_of_top_k(self.feature_importance,self.k)\n",
    "        return self\n",
    "    def transform(self,X):\n",
    "        return X[:,self.feature_indices_]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'feature_importances' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-153-863e79d2e0bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfeature_importances\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'feature_importances' is not defined"
     ]
    }
   ],
   "source": [
    "feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['median_house_value', 'median_income', 'total_rooms', 'housing_median_age']"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第三题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'full_pipeline' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-155-2ad0eb48a26e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m prepare_select_and_predict_pipeline = Pipeline([\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;34m(\u001b[0m\u001b[0;34m'preparation'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfull_pipeline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'feature_selection'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mTepFeatureSelector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeature_importances\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m'final_model'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mrnd_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m ])\n",
      "\u001b[0;31mNameError\u001b[0m: name 'full_pipeline' is not defined"
     ]
    }
   ],
   "source": [
    "prepare_select_and_predict_pipeline = Pipeline([\n",
    "    ('preparation',full_pipeline),\n",
    "    ('feature_selection',TepFeatureSelector(feature_importances,k)),\n",
    "    ('final_model',RandomForestRegressor(**rnd_search.best_params_))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'prepare_select_and_predict_pipeline' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-156-2987e7a75ac2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprepare_select_and_predict_pipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhousing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhousing_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'prepare_select_and_predict_pipeline' is not defined"
     ]
    }
   ],
   "source": [
    "prepare_select_and_predict_pipeline.fit(housing,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'prepare_select_and_predict_pipeline' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-158-e02e92d1359e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msome_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhousing_labels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"预测值:\\t\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprepare_select_and_predict_pipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msome_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"实际值:\\t\\t\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msome_labels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'prepare_select_and_predict_pipeline' is not defined"
     ]
    }
   ],
   "source": [
    "#从完整数据集中去前4个数据和标签，数据用于完整\n",
    "some_data = housing.iloc[:4]\n",
    "some_labels = housing_labels.iloc[:4]\n",
    "\n",
    "print(\"预测值:\\t\",prepare_select_and_predict_pipeline.predict(some_data))\n",
    "print(\"实际值:\\t\\t\",list(some_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第四题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'prepare_select_and_predict_pipeline' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-159-43baf04920f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m }]\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m grid_search_prep = GridSearchCV(prepare_select_and_predict_pipeline,param_grid,cv=5,\n\u001b[0m\u001b[1;32m      6\u001b[0m                                scoring = 'neg_mean_squared_error',verbose=2,n_jobs=4)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'prepare_select_and_predict_pipeline' is not defined"
     ]
    }
   ],
   "source": [
    "param_grid = [{\n",
    "    'preparation__num_pipline__attribs_adder__add_bedrooms_per_room':[True,False]\n",
    "}]\n",
    "\n",
    "grid_search_prep = GridSearchCV(prepare_select_and_predict_pipeline,param_grid,cv=5,\n",
    "                               scoring = 'neg_mean_squared_error',verbose=2,n_jobs=4)\n",
    "grid_search_prep.fit(housing,housing_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'grid_search_prep' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-160-f50515ca8f92>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrid_search_prep\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'grid_search_prep' is not defined"
     ]
    }
   ],
   "source": [
    "grid_search_prep.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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